Evaluating product potential

There are different ways to evaluate the potential of consumer products. I personally think of a product’s chances to succeed as being a function of the magnitude of the pain-point that is being addressed, the frequency of encountering said pain-point, and its density among a given demographic. Taken together, these three factors may help in assessing whether a given product would address a ‘real’ opportunity.

Now, it’s tempting to individually score, and subsequently average out, the three components (i.e. frequency, density, magnitude) so as to arrive at a concrete ‘opportunity score’ of sorts. This would, however, probably not make a whole lot of sense from a mathematical perspective.

It might further be worth pointing out that this framework pertains less to the size of the opportunity (e.g. whether it’s a million- or a billion-dollar idea) and more to the difficulty of its realisation. That is to say, a lower opportunity score (however which way this may actually be calculated) wouldn’t necessarily translate to smaller success relative to a company with a higher opportunity score (and vice versa). To illustrate, Booking.com may have a lower opportunity score than Pocket would (both are addressed below), but Booking.com’s market cap is an order of magnitude greater than Pocket’s. Still, a low opportunity score would, in my estimation, entail that it would be more difficult to realise the potential that’s been discovered (once you do, however, you may be golden).

The framework is meant as a conceptual tool. As such, it’s meant to show the relationship between the three individual factors, rather than give precise values for each. Still, to illustrate how the model works out in practice, I will address three concrete examples below. This won’t be very scientific, but will hopefully give a directional idea.

Also, it should go without saying that whether a given company will be able to seize said opportunity clearly depends on a variety of other critical factors, ranging from market-size and monetisation strategy to execution and team. Hence, I’d posit that a sufficiently high value for the composite of the three individual components would be far from sufficient for a start-up to succeed, but might be necessary for it to stand a chance in the first place — a prerequisite if you like.

Anyway, on to the examples then.

1. Booking.com

High-magnitude, low-frequency, high-density

Booking.com is an online accommodation booking website that has over 700,000 properties globally under contract and deals with 900,000 room-nights reservations each day. I’ll address the three individual factors.

Magnitude (high): How big is the pain-point that Booking.com addresses? When traveling, you’ll most likely need a place to stay. Now, you could do a whole lot of research yourself across a variety of websites of the individual hotels at your destination of choice. But the hotel market is a highly fragmented travel category, with hundreds of thousands of properties available worldwide. Hotel bookings are therefore well suited to online research and purchasing, but often leave potential travelers with hundreds of options to choose from. The combination of choice and variability may lead to a lack of confidence among users in the accuracy and comprehensiveness of flight and hotel data. Indeed, if there’d be a centralized solution that provides a succinct overview of the different options on offer, and if these results could then be filtered along the dimensions you care about (e.g. budget, number of stars, or the distance from downtown), then that would probably be a major improvement relative to the DIY alternative.

Frequency (low ): How often does a potential user encounter the pain-point Booking.com alleviates (let’s say annually)? In this case, the answer is fairly straightforward and roughly corresponds with the number of domestic or international trips per year. Although this clearly differs per country and depends on factors such as the level of income, number of days off, or local weather conditions, the average European goes on ~4 trips per year; the average American travels ~7 times per year (the former over-indexes on outbound trips, the latter on domestic tourism). As such, at least compared to products that provide daily utility, the frequency is rather low.

Density (high): How wide-spread might the pain-point be among a given population? It is estimated that 61% of the European population took part in tourism for personal purposes at least once in 2015. For simplicity’s sake, let’s leave business travel out of the equation and assume a similar number holds true for the American population. The pain-point is thus fairly well spread throughout the Western world.

Similar examples that would fit in the high-density, high-magnitude, low-frequency bracket may include real estate marketplaces (e.g. Zillow), car rental sites (e.g. RentalCars) , job-search platforms (e.g. MonsterBoard), or insurance solutions (e.g. Oscar).

In my estimation, part of Booking.com’s success can be explained by scoring above average on both magnitude and density, solving a significant problem that many people have (although comparatively less frequently so).

2. Pocket

High-frequency, low-magnitude, high-density

A good fit for this bracket, and coincidentally one of my favorite apps, would be Pocket. Whether it’s in our Twitter newsfeed, our e-mail inbox, or simply while browsing the Internet, we discover interesting content everywhere we go. Yet we don’t always have the time to read or watch something the moment we find it. Pocket is an easy way to save the content we may stumble upon online and have access to it later. Again, I’ll address the three factors.

Magnitude (low): In insolation, the pain of not having the time to immediately read an interesting article you came across may be pretty insignificant. You could jot its title down on a notepad or simply bookmark the link.

Frequency (high): But what seems meaningless in isolation can become meaningful in the aggregate. We’re on the web several hours a day. If you weren’t skimming through this piece right now, you’d probably be scanning something else online. Some studies say we spend more time on our phones and laptop than we do sleeping. The frequency with which we may encounter something that piques our interest, but which cannot be addressed immediately, is therefore high.

Density (high): While not all of us are glued to a screen for the better part of a day, I think it’s safe to say that most of us do have access to the web and, consequently, encounter the issue that pocket addresses — at least to some extent.

Similar examples that would fit in the low-magnitude, high-frequency, high-density bracket may include apps that have to do with daily productivity (DropBox), fitness (MyFitnessPal).. or your electric toothbrush.

I would argue that part of Pocket’s success can be explained by scoring above average on both frequency and density, solving a small problem that many people seem to encounter very frequently.

3. Bim-Bim Bikes

Low-frequency, low-magnitude, medium-density.

While I don’t mean to single out or pick on them specifically, I’m a bit less positive about the prospects for this online bike rental platform. To be sure, I wish them all the best, but it seems like the odds are stacked against them — at least according to this model it does.

Magnitude (low): At face value, the idea appears to make sense. The platform could be to bike rentals what online travel agencies are to accommodation. I really wonder how big the pain point is that they’re addressing, though. Empirically speaking, in the rare case I find myself in need of a bike while traveling, I don’t mind performing a quick google search, visiting the local tourist centre, or simply addressing a local to find out where the nearest bike-rental spot may be. What’s more, these type of spots usually make sure to be relatively central to main tourist hubs. And so given that I’d be interested in renting a bike in the first place, it wouldn’t be too much of a hassle to find one nearby (vs. if for some reason all bike rentals would have been situated in some far-flung suburb). I would also point out that there are simply less dimensions a bike is evaluated along relative to the accommodation equivalent, which just makes a platform for comparison less relevant.

Frequency (low): How often may travelers find themselves in a city without both a bike and the knowledge of where to get one? In this case, the answer is probably close to the number we’ve established for Booking.com and basically corresponds with the number of domestic or international trips per year. The frequency is again rather low.

Density (medium): What percentage of a given population find themselves looking to rent a bike, but without the knowledge of where to do so? For simplicity’s sake, I stated in the paragraph above that the number for frequency roughly equals the number we had identified for Booking.com. However, it would probably be unfair to equate the density number for Bim-Bim Bikes with Booking.com’s number for density. Doing so would assume that 1) you’d actually be in need of a bike in the first place and 2) you‘d be looking to rent a bike every time you’re traveling, which may not be the case. That is, unlike accommodation, a bike clearly isn’t a prerequisite to a successful trip. We said that 60% of Europeans travel. I think BimBimBikes would be lucky if about 15% of that subset translates to people interested in their solution.

In my estimation, it seems like BimBim Bikes has a fairly low chance of being successful, because they address a small problem that relatively few people encounter not so frequently.

Consequences

Now this is all fairly arbitrary, but I believe you get the gist. I think that a less than sufficient score for two of the three dimensions (however this may be defined) would decrease opportunity significantly. If you’re working on a product that scores low for one of the three, it follows that you better make sure you score relatively high on the other two (e.g. solving a small problem is fine, so long as enough people encounter said problem regularly).

Identifying the bracket you’re in has consequences. To give one example, if you’re high-magnitude, low-frequency, you’ll most likely find yourself spending a whole lot on user acquisition. It’s difficult to ensure your solution is top-of-mind right at the moment a potential customer needs it. One way to address this, of course, is to never be away from your customers’ awareness.

I reckon that this is why we are so familiar with Geico, its gecko, and their slogan, even though we’re rarely actually in need of their service (from a pure transaction perspective). Conversely, if you’re in the low-magnitude, high-frequency bracket, you’ll probably have a slightly easier time acquiring users. Even though the pain point is fairly low, your potential users may be reminded of the issue regularly — to the extent of ultimately being triggered to go look for a solution.

Anyway, I’m sure you may think this piece is stating the obvious or sounds completely arbitrary. I guess to some extent it is. Still, seeing as I feel writing is a good way to structure my thoughts, I guess it’s more meant to do just that than anything else. Though perhaps you’ll find it useful.